CoGNN: Towards Secure and Efficient Collaborative Graph Learning
Year: 2024
Type: conference-paper
Abstract: Collaborative graph learning represents a learning paradigm where multiple parties jointly train a graph neural network (GNN) using their own proprietary graph data. To honor the data privacy of all parties, existing solutions for collaborative graph learning are either based on federated learning (FL) or secure machine learning (SML). Although promising in terms of efficiency and scalability due to their distributed training scheme, FL-based approaches fall short in providing provable security guarantees and achieving good model performance. Conversely, SML-based solutions, while offering provable privacy guarantees, are hindered by their high computational and communication overhead, as well as poor scalability as more parties participate.
Institution: Tsinghua University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4405182557